Backpropagation is a fundamental method by which neural networks enhance their performance. For every batch of training data, neural networks conduct a "forward pass" through the network. Subsequently, they calculate the gradient of each neuron in each layer, starting from the output layer and working backward. Based on these gradients, they adjust the weights of the neurons in the direction that minimizes the loss function. Through millions of such iterative processes, neural networks gradually improve, enabling them to "learn" and better fit the training data.
